Adjacent Scales Research Articles

<p><strong>A new species of the genus <em>Norops</em> from Darién, Panama, with comments on <em>N. sulcifrons</em> (Cope 1899) (Reptilia, Squamata, Dactyloidae)</strong></p>

We describe the new species Norops triumphalis sp. nov. from Darién, Panama. Norops triumphalis differs from all congeners by having a combination of (1) smooth, bulging, subimbricate ventral scales; (2) a short tail, ratio tail length/SVL 1.54; (3) short hind legs, longest toe of adpressed hind leg reaching to ear opening, ratio shank length/SVL 0.24; (4) a lichenous body pattern; and (5) a very large yellowish orange dewlap in males. In external morphology, N. triumphalis is most similar to the species of the N. pentaprion group. Norops triumphalis differs from the other species in the N. pentaprion group, except N. sulcifrons, by having a very large orange male dewlap (vs. a large red or pink dewlap) and an unpigmented throat lining. Norops triumphalis differs from N. sulcifrons by having the supracaudal scales not forming a serrated crest (vs. a distinct serrated caudal crest present in N. sulcifrons), 4 supracaudal scales per segment (vs. 3 supracaudal scales per segment in N. sulcifrons), greatly enlarged outer postmental scales, about four times the size of adjacent medial scales (vs. moderately enlarged outer postmental scales, about twice the size of adjacent medial scales, in N. sulcifrons), and no enlarged postcloacal scales in males (vs. a pair of moderately enlarged postcloacal scales present in male N. sulcifrons). We further provide a standardized description and illustrations of the holotype of N. sulcifrons.

The trunk lateral line system and its innervation in Mugil cephalus (Mugilidae: Mugiliformes)

The trunk lateral line system and its innervation were examined in Mugil cephalus. Almost every scale had an open longitudinal groove that housed a superficial neuromast (SN), the total number of SNs being 550 (including 55 on the caudal fin). Grooved lateral line scales constituted 14 longitudinal rows (14 lateral lines), although each groove was independent, not forming a continuous common groove along adjacent scales. The lateral ramus of the posterior lateral line nerve gave off four characteristic ramules: the anteriormost two dorsal ramules extended strongly backward, innervating SNs on the dorsal surface of the trunk; the anteriormost ventral ramule was extensively ramified to innervate SNs on the anterior flank, passing ventrally in front of the pectoral fin base; and the fifth ventral ramule extended posteriorly close to the anal fin origin, innervating SNs on the ventral half of the middle flank. The remaining SNs were innervated by 29 dorsal, 33 lateral and 25 ventral ramules, excepting those on the caudal fin. The innervation pattern revealed that the sixth lateral line (from uppermost) was homologous with that typically found in teleosts, the others being considered as additional. The sixth lateral line was anteriorly regressive, suggesting that the high insertion of the pectoral fin on the trunk is a secondary condition.

Use of Fuzzy rainfall–runoff predictions for claypan watersheds with conservation buffers in Northeast Missouri

Summary Fuzzy rainfall–runoff models are often used to forecast flood or water supply in large catchments and applications at small/field scale agricultural watersheds are limited. The study objectives were to develop, calibrate, and validate a fuzzy rainfall–runoff model using long-term data of three adjacent field scale row crop watersheds (1.65–4.44 ha) with intermittent discharge in the claypan soils of Northeast Missouri. The watersheds were monitored for a six-year calibration period starting 1991 (pre-buffer period). Thereafter, two of them were treated with upland contour grass and agroforestry (tree + grass) buffers (4.5 m wide, 36.5 m apart) to study water quality benefits. The fuzzy system was based on Mamdani method using MATLAB 7.10.0. The model predicted event-based runoff with model performance coefficients of r 2 and Nash–Sutcliffe Coefficient (NSC) values greater than 0.65 for calibration and validation. The pre-buffer fuzzy system predicted event-based runoff for 30–50 times larger corn/soybean watersheds with r 2 values of 0.82 and 0.68 and NSC values of 0.77 and 0.53, respectively. The runoff predicted by the fuzzy system closely agreed with values predicted by physically-based Agricultural Policy Environmental eXtender model (APEX) for the pre-buffer watersheds. The fuzzy rainfall–runoff model has the potential for runoff predictions at field-scale watersheds with minimum input. It also could up-scale the predictions for large-scale watersheds to evaluate the benefits of conservation practices.

Action recognition by spatio-temporal oriented energies

In this paper, we present a unified representation based on the spatio-temporal steerable pyramid (STSP) for the holistic representation of human actions. A video sequence is viewed as a spatio-temporal volume preserving all the appearance and motion information of an action in it. By decomposing the spatio-temporal volumes into band-passed sub-volumes, the spatio-temporal Laplacian pyramid provides an effective technique for multi-scale analysis of video sequences, and spatio-temporal patterns with different scales could be well localized and captured. To efficiently explore the underlying local spatio-temporal orientation structures at multiple scales, a bank of three-dimensional separable steerable filters are conducted on each of the sub-volume from the Laplacian pyramid. The outputs of the quadrature pair of steerable filters are squared and summed to yield a more robust oriented energy representation. To be further invariant and compact, a spatio-temporal max pooling operation is performed between responses of the filtering at adjacent scales and over spatio-temporal neighbourhoods. In order to capture the appearance, local geometric structure and motion of an action, we apply the STSP on the intensity, 3D gradients and optical flow of video sequences, yielding a unified holistic representation of human actions.Taking advantage of multi-scale, multi-orientation analysis and feature pooling, STSP produces a compact but informative and invariant representation of human actions. We conduct extensive experiments on the KTH, UCF Sports and HMDB51 datasets, which shows the unified STSP achieves comparable results with the state-of-the-art methods.

Scale analysis of turbulent channel flow with varying pressure gradient

In this paper orthogonal wavelet transformations are applied to decompose experimental velocity signals in fully developed channel flows with varying pressure gradient into scales. We analyze the time series from turbulent data, to obtain the statistical characteristics, correlations between the adjacent scales and the principal scale of coherent structures in different scales by wavelet transformations. The results show that, in the counter gradient transport (CGT) region, skewness factors and flatness factors deviate strongly from the corresponding values of Gaussian distribution on certain scales. PDFs on each scale confirm this observation. Scale-scale correlations show further that the fluctuations on some certain special scales are more intermittent than nearby. Principal scale of coherent structure is coincident with the scales on which the statistical properties depart from Gaussian distribution. These features are the same for different families of wavelets, and it also shows some different features in the region between favorable pressure gradient and adverse pressure gradient.

Possible causes of the variation in fractal dimension of the perimeter during the tropical cyclone Dan motion

We calculated the fractal dimensions D b of the perimeter of tropical cyclone (TC) Dan based on the satellite GMS-5 infrared sensor images from 1800 UTC, 1 October 1999 to 1200 UTC, 9 October 1999. The fractal dimensions D b were used to characterize objectively the temporal change of TC complex structure. Our results show that the change of fractal dimension during TC Dan motion can be divided into three stages. The statistically significant difference does not exist either between D m1 and D L or between D m3 and D L, but it exists between D m2 and D L, where D mi denotes the mean value of D b in i-th stage (i=1, 2 and 3); D L denotes Lovejoy’s fractal dimension calculated based on satellite and radar data within the size range (1−1.2×106 km2), which is used as a “normal value” of the fractal dimension of the cumulus cloud perimeter for the global tropical region. TC Dan turns to the north from the west abruptly at the end of the second stage. The emergence of the second stage with high fractal dimensions may be viewed as a possible premonition for the track turning. Our results also show that there are two kinds of processes resulting in the translation from the first stage to the second stage. One is the interaction of TC circulation and an adjacent small scale convective cloud cluster, causing to the complexity increase of a local segment of the perimeter. The other includes the fragmentation of a strong convective area within the TC inner region, the self-organization of the small strong convective cloud clusters, the emergence, development, and merger of the small scale non-convective holes, and the formation of a gap of the perimeter, causing to the complexity increase of the whole TC perimeter.

Novel Ensemble Analytic Discrete Framelet Expansion for Machinery Fault Diagnosis

As the celebrated "mathematical scope", the multi-resolution analyzing capacity of wavelet transform(WT) plays an important role in condition monitoring and fault diagnosis of mechanical equipment. However, it has proven that the effectiveness of WT is hampered by several negative factors, such as shift-sensitiveness, significant energy leakage, and the fixed dyadic "frequency-sale" paving. Especially, the dyadic "frequency-sale" paving creates inevitable deficiency in identifying mechanical signatures located in transition areas of adjacent wavelet scales. A novel "time-sale" analysis methodology, named as derived ensemble analytic framelet(DEAF), based on overcomplete wavelet tight frame, is proposed. The DEAF is developed based on the existing dual tree complex wavelet transform(DTCWT). The DEAF starts from a selected DTCWT basis, and combines it with a hybrid augmented tree-structured filter-bank, which results in quasi analytic wavelet packet decomposition(QAWPD). With the results of QAWPT, an ensemble wavelet packet generating strategy is applied such that an unprecedented implicit wavelet packet tight frame(IWPTF) containing pseudo dyadic wavelet packets is obtained. With the combination of QAWPD and IWPTF, the proposed DEAF can be derived which possesses the "frequency-sale" paving characterized by continued time-frequency refinement of analysis centers. The proposed technique is applied to the mechanical signature analysis of an engineering application to validate its superiority compared with the existing methods.

Fingerprint Classification Combining Curvelet Transform and Gray-Level Cooccurrence Matrix

Fingerprint classification is an important indexing scheme to reduce fingerprint matching time for a large database for efficient large-scale identification. The abilities of Curvelet transform capturing directional edges of fingerprint images make the fingerprint suitable to be classified for higher classification accuracy. This paper presents an efficient algorithm for fingerprint classification combining Curvelet transform (CT) and gray-level cooccurrence matrix (GLCM). Firstly, we use fast discrete Curvelet transform warping (FDCT_WARPING) to decompose the original image into five scales Curvelet coefficients and construct the Curvelet filter by Curvelet coefficients relationship at adjacent scales to remove the noise from signals. Secondly, we compute the GLCMs of Curvelet coefficients at the coarsest scale and calculate 16 texture features based on 4 GLCMs. Thirdly, we construct 49 direction features of Curvelet coefficients at the other four scales. Finally, fingerprint classification is accomplished byK-nearest neighbor classifiers. Extensive experiments were performed on 4000 images in the NIST-4 database. The proposed algorithm achieves the classification accuracy of 94.6 percent for the five-class classification problem and 96.8 percent for the four-class classification problem with 1.8 percent rejection, respectively. The experimental results verify that proposed algorithm has higher recognition rate than that of wavelet-based techniques.

Spatially adaptive despeckling of SAR image using bivariate thresholding in directionlet domain

A novel spatially adaptive despeckling algorithm is presented for a synthetic aperture radar (SAR) image by taking into account the statistical interscale dependency of the directionlet transform (DT) coefficients. The DT is first computed along the dominant directions of the spatially segmented image. The signal and the noise coefficients are modelled using bivariate distributions which take into account the statistical dependence between the adjacent scale coefficients. The nonlinear threshold functions derived from the models employing a maximum a posteriori estimator are then used for estimating the noise-free coefficients. The experimental results show that the proposed method outperforms similar methods in terms of numerical and perceptual quality.

Blind Assessment of Wavelet-Compressed Images Based On Subband Statistics of Natural Scenes

This paper presents a no-reference image quality assessment metric that makes use of the wavelet subband statistics to evaluate the levels of distortions of wavelet-compressed images. The work is based on the fact that for distorted images the correlation coefficients of the adjacent scale subbands change proportionally with respect to the distortion of a compressed image. Subband similarity is used in this work to measure the correlations of the adjacent scale subbands of the same wavelet orientations. The higher the image quality is (i.e., less distortion), the greater the cosine similarity coefficient will be. Statistical analysis is applied to analyze the performance of the metric by evaluating the relationship between the human subjective assessment scores and the subband cosine similarities. Experimental results show that the proposed blind method for the quality assessment of wavelet-compressed images has sufficient prediction accuracy (high Pearson Correlation Coefficient, PCCs), sufficient prediction monotonicity (high Spearman Correlation Coefficient SCCs) and sufficient prediction consistency (low outlier ratios) and less running time. It is simple to calculate, has a clear physical meaning, and has a stable performance for the four image databases on which the method was tested.

Directional dissimilarity of transitional functions: Volume energy density factor

Scale segmentation of the SI system of measurement has necessitated the use of transitional function for connecting the results of the segmented scales. The state-of-the-art of measurement in the 1800s is reflected by the coarseness of scale segmentation. By up-to-day standard of nanonization, scale refinement is necessary to account for the interactive benefits of multiscaling, the building block of which is dual scaling, say macro-micro or micro-nano. The directional dissimilarity between two adjacent scales, say macro → micro and micro → macro, arises naturally in physical processes. An index Λ is defined to measure the severity of directional dissimilarity that may prevail in the volume energy density factor (VEDF) for macro → micro. A similar index Ω applies to micro → macro. Both A and Ω reflect the combined effects of loading, material and geometry. The macro-micro transition of VEDFmacro can differ from the micro-macro transition of VEDFmicro. The relation between the macrostress intensity factor and VEDFmacro is incompatible with that for the microstress intensity factor and VEDFmicro. The stress intensity factor, a monoscale concept, will not hold for both the macro- and micro-scale. To begin with, the units may differ. The same holds for the volume energy density factor. The irreconcilability can be resolved by an argument of dimensional compatibility, provided that the agreement is to preserve the monoscale definitions. These considerations call for a scrutiny of the crack growth data whose scatter may be contributed by the directional effects of scale transition.

SAR image despeckling using adaptive multiscale products thresholding in directionlet domain

A directionlet transform (DT)-based multiscale products thresholding is presented which uses a generalised cross-validation (GCV) technique for synthetic aperture radar image despeckling. DT has gained popularity over the past few years as an anisotropic, critically sampled and perfect reconstruction transform with directional vanishing moments along any two directions. In this reported work, the multiscale correlation of DT is exploited by multiplying the adjacent scale coefficients to enhance edge structures while weakening noise. An optimal subband adaptive threshold based on GCV is then computed using this multiscale product. The despeckled image is finally synthesised by using the threshold applied DT coefficients. The employment of DT results in the significant features in images evolving with high magnitudes across scales, while the noise decays rapidly. The proposed scheme outperforms many of the traditional despeckling schemes in terms of speckle reduction and edge preservation.

Real-time image fusion based on morphological un-decimated wavelets

An efficient Morphological Un-Decimated Wavelet(MUDW) transform with more delicate and accurate multi-scale decomposition performance that suites real-time image fusion was proposed.It took the average of dilation and erosion as the analysis operator,and the difference of adjacent scale images as the detail image.Size-increasing structure elements were adopted to get better fusion result.Due to the simplicity of dilation and erosion operator,computation time is shorter than other real-time algorithms.Furthermore,a factor was added during reconstruction,to obtain an obvious enhancement effect.The experimental results show that the new method outperforms other real-time algorithms.

A multiscale products technique for denoising of DNA capillary electrophoresis signals

Since noise degrades the accuracy and precision of DNA capillary electrophoresis (CE) analysis, signal denoising is thus important to facilitate the postprocessing of CE data. In this paper, a new denoising algorithm based on dyadic wavelet transform using multiscale products is applied for the removal of the noise in the DNA CE signal. The adjacent scale wavelet coefficients are first multiplied to amplify the significant features of the CE signal while diluting noise. Then, noise is suppressed by applying a multiscale threshold to the multiscale products instead of directly to the wavelet coefficients. Finally, the noise-free CE signal is recovered from the thresholded coefficients by using inverse dyadic wavelet transform. We compare the performance of the proposed algorithm with other denoising methods applied to the synthetic CE and real CE signals. Experimental results show that the new scheme achieves better removal of noise while preserving the shape of peaks corresponding to the analytes in the sample.

Adaptive redundant multiwavelet denoising with improved neighboring coefficients for gearbox fault detection

Gearbox fault detection under strong background noise is a challenging task. It is feasible to make the fault feature distinct through multiwavelet denoising. In addition to the advantage of multi-resolution analysis, multiwavelet with several scaling functions and wavelet functions can detect the different fault features effectively. However, the fixed basis functions not related to the given signal may lower the accuracy of fault detection. Moreover, the multiwavelet transform may result in Gibbs phenomena in the step of reconstruction. Furthermore, both traditional term-by-term threshold and neighboring coefficients do not consider the direct spatial dependency of wavelet coefficients at adjacent scale. To overcome these deficiencies, adaptive redundant multiwavelet (ARM) denoising with improved neighboring coefficients (NeighCoeff) is proposed. Based on symmetric multiwavelet lifting scheme (SMLS), taking kurtosis—partial envelope spectrum entropy as the evaluation objective and genetic algorithms as the optimization method, ARM is proposed. Considering the intra-scale and inter-scale dependency of wavelet coefficients, the improved NeighCoeff method is developed and incorporated into ARM. The proposed method is applied to both the simulated signal and the practical gearbox vibration signal under different conditions. The results show its effectiveness and reliance for gearbox fault detection.

SAR Image Despeckling with Adaptive Multiscale Products Based on Directionlet Transform

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise generated by radar coherent wave. In this paper, a new despeckling algorithm based on directionlets using multiscale products is proposed. We first take an anisotropic directionlet transform on the logarithmically transformed SAR images and multiply the coefficients at adjacent scales to enhance the details of image under consideration. Then, different from traditional thresholding methods, a threshold is applied to the multiscale products of the directionlet coefficients to suppress noise. Since the multiplication amplifies the significant features of signal and dilute noise, the proposed method reduces noise effectively while preserving edge structures. Finally, we compare the performance of the proposed algorithm with other despeckling methods applied to synthetic image and real SAR images. Experimental results demonstrate the effectiveness of the proposed method in SAR images despeckling.

A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising

In this work, the authors present an effective denoising method to attempt reducing the noise in mammographic images. The method is based on using hierarchical correlation of the coefficients of discrete stationary wavelet transforms. The features of the proposed technique include iterative use of undecimated multi-directional wavelet transforms at adjacent scales. To validate the proposed method, computer simulations were conducted, followed by its applications to clinical mammograms. Mutual information originating from information theory was used as an evaluation measure for selection of an optimal wavelet basis function. We examined the performance of the proposed method by comparing it with the conventional undecimated discrete wavelet transform (UDWT) method in terms of processing time-consuming and image quality. Our results showed that with the use of the proposed method the computation time can be reduced to approximately 1/10 of the conventional UDWT method consumed. The results of visual assessment indicated that the images processed with the proposed UDWT method showed statistically significant superior image quality over those processed with the conventional UDWT method. Our research results demonstrate the superiority and effectiveness of the proposed approach.

Application Study on of Wavelet Analysis during Compression Ruptrue Procedure of Coal Sample

This paper study the acoustic signal characteristics of coal and rock mass under uniaxial compression by the tests of acoustic emission(AE),which obtain the variation of AE counts and energy.Compressing on original data use wavelet analysis,and extracting the characteristics of signal with the wavelet sym-basis,then decomposing and remodeling the wavelet coefficients,the negative singularity degree indicators of adjacent scales can be got.The results showed that the wavelet transform modulus maximum used to identify the negative singularity degree can achieve the position of the frequency changes point of the acoustic signal,which is of certain guiding significance for prediction of the uniaxial compression process.

Synthetic Turbulence Constructed by Self-Learning Fractal Interpolation

A self-learning fractal interpolation algorithm to construct synthetic fields with statistical properties close to real turbulence is proposed. Different from our previous work [Phys. Rev. E 84 (2011) 026328, 82 (2010) 036311], the position mapping and stretching factors between the adjacent large and small scales are learned from the initial information. Using this method, a turbulence-like field with K41 spectra and without dissipation is constructed well through a coarse grid velocity signal from one experiment's data. After filtering the interpolated signal appropriately, the probability distribution of velocity, velocity structure functions and the anomalous scaling law of the synthetic field are close to those of the original signal.

Switching Algorithm for Maglev Train Double-Modular Redundant Positioning Sensors

High-resolution positioning for maglev trains is implemented by detecting the tooth-slot structure of the long stator installed along the rail, but there are large joint gaps between long stator sections. When a positioning sensor is below a large joint gap, its positioning signal is invalidated, thus double-modular redundant positioning sensors are introduced into the system. This paper studies switching algorithms for these redundant positioning sensors. At first, adaptive prediction is applied to the sensor signals. The prediction errors are used to trigger sensor switching. In order to enhance the reliability of the switching algorithm, wavelet analysis is introduced to suppress measuring disturbances without weakening the signal characteristics reflecting the stator joint gap based on the correlation between the wavelet coefficients of adjacent scales. The time delay characteristics of the method are analyzed to guide the algorithm simplification. Finally, the effectiveness of the simplified switching algorithm is verified through experiments.